• Siddhant Sharma
• Akhilesh A. Waoo

A significant proportion of the Clients are making their product purchases via the internet platform. The Client, in this case, does not buy the thing online but does research online before making a purchase. Consumers' decisions are heavily influenced by the reviews and ratings that other people have given certain products. If a product receives positive ratings and comments from customers, there is a greater likelihood that it will be purchased. There are situations when businesses or individuals may provide ratings that are either fake or incorrect. The artificial intelligence-based machine learning approaches can create a prediction model or make forecasts about what reviews will be truthful and which will be incorrect. Using a decision tree machine learning technique, this study gives an examination of customer behavior in online retailing. Python Spyder is the platform that is used for simulation, and the results of the simulation demonstrate an increase in the accuracy of the prediction model.

Machine learning, E-Commerce, Python, Accuracy, Error rate

"Decision Tree Machine Learning Approach for Customer Behavior Analysis on Online Product Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e768-e772, March-2023, Available :http://www.jetir.org/papers/JETIR2303498.pdf

"Decision Tree Machine Learning Approach for Customer Behavior Analysis on Online Product Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe768-e772, March-2023, Available at : http://www.jetir.org/papers/JETIR2303498.pdf